cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Arief Hidayat
Contact Email
arief.hidayat@unwahas.ac.id
Phone
+628156529309
Journal Mail Official
jinformatika@unwahas.ac.id
Editorial Address
JL. Menoreh Tengah X / 22, Sampangan, Gajahmungkur, Sampangan, Gajahmungkur, Kota Semarang, Jawa Tengah 50232
Location
Kota semarang,
Jawa tengah
INDONESIA
Jurnal Informatika dan Rekayasa Perangkat Lunak
Published by Universitas Wahid Hasyim Semarang
ISSN : 26562855     EISSN : 26855518     DOI : http://dx.doi.org/10.36499/jinrpl
Core Subject : Science,
Computer Science & IT
Journal of Informatics and Software Engineering accepts scientific articles in the focus of Informatics. The scope can be: Software Engineering, Information Systems, Artificial Intelligence, Computer Based Learning, Computer Networking and Data Communication, and Multimedia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Perangkat Lunak
Articles 20 Documents
 1   2 
Search results for , issue "Vol. 6 No. 2 (2024): September" : 20 Documents clear
Identifikasi Kesegaran Ikan Bandeng Non-kontak menggunakan MobileNetV2 Hidayatullah, Achmad Nasrul; Prasetyo, Eko; Purbaningtyas, Rani
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Milkfish is a superior commodity in several districts in Indonesia, namely Sidoarjo, Semarang, and Banten. This fish is also a favorite of Indonesians because it is nutritious and affordable. Therefore, for milkfish processed product business people, the freshness of milkfish is an important parameter because the freshness of the fish affects the quality of the processed products. Manual fish sorting is a problem when the number of fish is vast because it is prone to errors due to fatigue. In addition, manual fish sorting is also wasteful and time-consuming. Therefore, a non-contact automatic system is needed to identify fish freshness based on digital images. This study uses the Convolutional Neural Network (CNN) model to develop an application for milkfish freshness identification. We applied the MobileNetV2 model to identify the freshness of milkfish into three freshness classes, namely very fresh, fresh, and not fresh. The application uses the MobileNetV2 model on 312 milkfish images. The freshness classification performance reached 95%, 70%, and 80% in the high-fresh, fresh, and not-fresh classes, respectively. The global accuracy of the system reached 81.6%, indicating that the application can work well. From the experiments and analysis conducted, it can be concluded that the system has good capabilities in identifying fish freshness.
Pengenalan Gestur Bahasa Isyarat Indonesia dengan Mediapipe Keypoints Dewanto, Febrian Murti; Harjanta, Aris Tri Jaka; Nada, Noora Qotrun; Herlambang, Bambang Agus
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Difficulty in communication is an obstacle for deaf friends who cannot learn the language orally or acquire normal speech skills. The development of sign language gesture recognition technology is an important step to improve accessibility and social integration for the deaf community. The use of MediaPipe Holistic Keypoints and deep learning techniques provides significant potential in recognizing and understanding sign language gestures. The main objective of this study is to classify Indonesian Sign Language (BISINDO) gestures using MediaPipe Holistic Keypoints and a deep learning approach to identify basic words in sign language. By extracting features using mediapipe holistic and sending them to the LSTM 6 hidden layer model with 70:30 split train test and 250 epochs, an accuracy of 68% was produced. This is due to the limited number of datasets taken for the study.
Implementasi dan Perancangan Sistem Informasi Penjualan Vapestore Berbasis Mobile Flutter Ripai, Rizki; Fauzi, Yudiansyah; Sidik, Fazar; Pari, Riki Aldi; Hamdan, Rifky Aditia
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The sale of vape products has become a rapidly growing industry, with increasing demand from a diverse range of consumers. To keep up with these developments, the existence of an efficient and easily accessible sales information system is crucial for vape store owners. This study aims to implement and design a mobile-based sales information system for Vapestore using Flutter. The system development method used in this research is the iterative software development method. Flutter was chosen as the development platform due to its ability to create mobile applications with responsive user interfaces and efficient cross-platform deployment. The designed information system will include features such as inventory management, customer management, sales report generation, and online payment integration. By using Flutter, it is expected that this application can be easily accessed through various mobile devices, such as smartphones and tablets, allowing store owners to manage their sales efficiently wherever and whenever needed. Additionally, with the online payment integration feature, customers will also experience a more convenient and faster shopping process. This research is expected to provide a positive contribution to vape store owners in improving their operational efficiency and enhancing the customer shopping experience. Furthermore, the results of this study may serve as a reference for future research in the development of more advanced sales information systems that are tailored to the continuously evolving market needs.
Implementasi Profile Matching pada Sistem Pendukung Keputusan Seleksi Peserta Tenda Kewirausahaan Setiawan, Aries; Nuryanto, Imam; Mintorini, Ery; Hidajat, Moch. Sjamsul; Farida, Ida; Widjajanto, Budi; Prasetya, Jaka; Lewa, Andi Hallang; Karmila, Karmila
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

One of the programs from the Entrepreneurship Unit is the entrepreneurial tent participant program. In the manual assessment process, participant selection is only based on the type of entrepreneurial product that will be offered. However, this does not get maximum selection results because if the existing selection only uses one variable component and the assessment tends to contain elements that have no potential. One decision making method that has weight in its calculations is profile matching. Profile matching works by assigning a standard value to each variable and a weighted value is also assigned to the variable. Next, look for differences in participant scores and standard scores for each variable. The ranking results resulting from profile matching are a combination of several variables with different weight levels. Therefore, in assessing the selection of entrepreneurial tent participants, it is best to use the existing calculation pattern using the profile matching method. The weight of each variable is determined by the decision maker, in this case the head of Entrepreneurship. With different percentage weight values ​​for each variable, it will provide assessment results that are in accordance with the level of competency of the entrepreneurial tent selection participants.
Model Hybrid Random Forest dan Information Gain untuk meningkatkan Performa Algoritma Machine Learning pada Deteksi Malicious Software Rafrastara, Fauzi Adi; Ghozi, Wildanil; Sani, Ramadhan Rakhmat; Handoko, L. Budi
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The evolution of malware, or malicious software, has raised increasing concerns, targeting not only computers but also other devices like smartphones. Malware is no longer just monomorphic but has evolved into polymorphic, metamorphic, and oligomorphic forms. With this massive development, conventional antivirus software is becoming less effective at countering it. This is due to malware's ability to propagate itself using different fingerprint and behavioral patterns. Therefore, an intelligent machine learning-based antivirus is needed, capable of detecting malware based on behavior rather than fingerprints. This research focuses on the implementation of a machine learning model for malware detection using ensemble algorithms and feature selection to achieve optimal performance. The ensemble algorithm used is Random Forest, evaluated and compared with k-Nearest Neighbor and Decision Tree as state-of-the-art methods. To enhance classification performance in terms of processing speed, the feature selection method applied is Information Gain, with 22 features. The highest results were achieved using the Random Forest algorithm and Information Gain feature selection method, reaching a score of 99.0% for accuracy and F1-Score. By reducing the number of features, processing speed can be increased by almost fivefold.
Penerapan Recursive Feature Elimination (RFE) pada Tree-Based Classifier untuk Identifikasi Risiko Diabetes Maori, Nadia Annisa; Azizah, Noor
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Diabetes mellitus is a common chronic disease with significant global impact. Early identification of individuals at high risk of developing diabetes is critical for the prevention and management of the disease. This study explores the use of Recursive Feature Elimination (RFE) in decision tree-based classifiers to improve the accuracy of diabetes risk prediction. The Pima Indians Diabetes Database (PIDD) dataset was used as the database, and algorithms such as Decision Tree, Random Forest, Gradient Boosting, and Xtreme Gradient Boosting were tested. The results showed that the application of RFE improved the model accuracy, with Random Forest and Gradient Boosting achieving the highest accuracy of 77.27%. RFE also successfully identified the most relevant features, reduced the risk of overfitting, and improved model interpretability. This study provides a strong foundation for the development of more effective predictive tools in diabetes management and prevention. Future studies are recommended to test the generalizability of this approach to a wider dataset and in various clinical contexts.
Deteksi Serangan Denial of Service (DoS) dan Spoofing pada Internet of Vehicles menggunakan Algoritma K-Nearest Neighbor (KNN) Ghozi, Wildanil; Rafrastara, Fauzi Adi; Sani, Ramadhan Rakhmat; Abdussalam, Abdussalam
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The implementation of Internet of Things (IoT) technology in motor vehicles has been increasing over time and is known as the Internet of Vehicles (IoV). IoV is becoming more essential to society as it provides comfort, safety, and efficiency in driving. Unfortunately, the use of internet technology in IoV brings the potential for cyber-attacks, such as Denial of Service (DoS) and Spoofing. Intrusion Detection Systems in IoV have not yet fully matured, as this technology is still relatively new. Therefore, the potential threats and their significant impact make research on this topic urgently needed. This study aims to evaluate the performance of the k-Nearest Neighbor (kNN) classification algorithm in detecting cyber-attacks on IoV. The predicted classes in this study consist of six categories: Benign, DoS, Gas-Spoofing, Steering Wheel-Spoofing, Speed-Spoofing, and RPM-Spoofing. These two types of attacks on IoV (DoS and Spoofing) pose risks to the operational safety of vehicles, which can endanger drivers and other road users. The dataset used is a public dataset called CIC IoV2024. The performance of the kNN algorithm is also compared to three other state-of-the-art algorithms, including Naïve Bayes, Deep Neural Network, and Random Forest. The results show that k-Nearest Neighbor (kNN) achieved the best performance with a score of 98.7% for both accuracy and F1-Score metrics. kNN outperformed Naïve Bayes, which ranked second with a score of 98.1% accuracy and 98.0% F1-Score. Thus, the kNN algorithm can be recommended as a classifier in the development of an intrusion detection system for IoV
Rancang Bangun Sistem Informasi Manajemen Organisasi Kemahasiswaan Berbasis Web di UNISNU Jepara Pratama, Andrian Dico; Azizah, Noor; Sabilla, Alzena Dona
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In today's digital era, Management Information Systems are becoming increasingly important for organizations and businesses. Management Information Systems can help organizations remain competitive and thrive in an increasingly complex and dynamic era. However, in student organizations within Unisnu Jepara, the management process is still carried out with manual practices and using paper media. Where writing on paper media requires a lot of time and energy, moreover the need for information in the organization is certainly abundant. Not to mention the risk of material misuse and loss of data in the future if not managed properly and correctly. The method used by researchers in this study is the Waterfall method which has 5 stages starting from Requirement Analysis, System and Software Design, Implementation and Unit Testing, System Testing and Integration, and Operation and Maintenance. The result of this research is the creation of an organizational management information system at Unisnu Jepara which is expected to help improve efficiency in the operational and management processes of student organizations at the Islamic University of Nahdlatul Ulama Jepara.
Implementasi Business Intelligence untuk menganalisis Perkembangan Akademik Mahasiswa di Program Studi Sistem Informasi UNISNU Jepara Margaretha, Sintikhe Novia; Azizah, Noor; Sabilla, Alzena Dona
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research aims to build a Business Intelligence (BI)-based academic monitoring system in the Unisnu Jepara Information Systems Study Program to improve the efficiency and quality of decision making. The method used involves analyzing student academic data with the help of Microsoft Power BI for data processing. Data is taken from the Academic Information System (SIAKAD) and processed to produce visualizations in the form of informative dashboards. The research results show visualization of academic data which includes the number of students, academic status, average GPA, study period, and graduate success. This dashboard makes it easier to monitor and analyze academic data, supports better decision making, and improves the quality of education in the Unisnu Jepara Information Systems Study Program. This research makes aware how important BI implementation is in optimizing academic data management and strategic decision making in
Analisis Pengaruh Media Sosial terhadap Produktivitas Akademik Mahasiswa menggunakan Metode Decision Tree dan Random Forest Murwaningtyas, Chatarina Enny; Kristiamita, Angel; Putri, Agatha Lintang Antika Ika; Puspaningrum, Fibelia Dwi; Mahanani, Carolina Dhinda Putri
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aims to evaluate the impact of social media usage on the academic productivity of Universitas Sanata Dharma Yogyakarta students, measured through their Grade Point Average (GPA). The methods employed involve two machine learning models: Decision Tree and Random Forest. The data were processed using outlier-resistant scaling techniques and data balancing through oversampling. The results show that the Random Forest model outperformed with an accuracy, precision, recall, and F1-score of 90% each. Meanwhile, the Decision Tree model achieved 80% accuracy, with a precision of 86%, recall of 80%, and F1-score of 82%. Feature importance analysis revealed that 'Faculty' and 'Gender' are the most significant factors in predicting students' GPA. This study concludes that employing Random Forest with data balancing techniques can enhance prediction accuracy and reliability, providing insights into the optimal use of social media to improve students' academic productivity.

Page 2 of 2 | Total Record : 20

 1   2 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 7 No. 2 (2025): September Vol. 7 No. 1 (2025): Maret Vol. 6 No. 2 (2024): September Vol 6, No 2 (2024): September Vol 6, No 1 (2024): Maret Vol 5, No 2 (2023): September Vol 5, No 1 (2023): Maret Vol 4, No 2 (2022): September Vol 4, No 1 (2022): Maret Vol 3, No 2 (2021): September Vol 3, No 1 (2021): Maret Vol 2, No 2 (2020): September Vol 2, No 1 (2020): Maret Vol 1, No 2 (2019): September Vol 1, No 1 (2019): Maret More Issue